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Guangzhi Tang

4 accepted papers

2020

Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control

CoRL 2020

The energy-efficient control of mobile robots has become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by their limited on-board resources. An emerging non-Von Neumann model of intelligence, whe

2020

Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware

IROS 2020poster

Energy-efficient mapless navigation is crucial for mobile robots as they explore unknown environments with limited on-board resources. Although the recent deep rein-forcement learning (DRL) approaches have been successfully applied to navigation, their high energy consumption limits their use in sev…

Cited by 98Source ↗code ↗Scholar ↗
2019

Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM

IROS 2019poster

Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of specialized neurons, exhibiting asynchronous computations and event-based communications, with very low energy consumption. We p…

Cited by 124Source ↗Scholar ↗